Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 1101–1110 of 1718 papers

Title	Date	Tasks	Status
Few-Shot Teamwork	Jul 19, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior	Jul 12, 2022	Multi-agent Reinforcement LearningSociology	—Unverified
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework	Jul 12, 2022	Multi-agent Reinforcement LearningPolicy Gradient Methods	—Unverified
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning	Jul 8, 2022	GPUMulti-agent Reinforcement Learning	—Unverified
Decentralized scheduling through an adaptive, trading-based multi-agent system	Jul 5, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems	Jun 28, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System	Jun 27, 2022	Multi-agent Reinforcement LearningReinforcement Learning (RL)	—Unverified
Functional Optimization Reinforcement Learning for Real-Time Bidding	Jun 25, 2022	AttributeMulti-agent Reinforcement Learning	—Unverified
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning	Jun 22, 2022	counterfactualMulti-agent Reinforcement Learning	CodeCode Available
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems	Jun 21, 2022	Multi-agent Reinforcement Learning	—Unverified

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified